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Proposal of a New Approach Using Deep Learning for QR Code
Embedding
Kanaru Kumabuchi
a
and Hiroyuki Kobayashi
b
Osaka Institute of Technology University, Osaka, Japan
Keywords:
Deep Learning, Image Hiding, Image Processing.
Abstract:
The purpose of this research is to enhance the technique of embedding QR codes into arbitrary images using
deep learning. Previous approaches faced the issue of compromising the quality when embedding QR codes
into arbitrary images. We address this problem by proposing a deep learning model and learning method
that can improve the quality of embedded images and accurately recover QR codes. Specifically, we design
a new model using deep learning that embeds QR codes into images while minimizing the degradation of
image quality. The effectiveness of the proposed model and learning method is validated through experiments,
demonstrating the enhancement of image quality in the embedded images and accurate QR code recovery.
1 INTRODUCTION
In recent years, with the widespread use of the inter-
net, exchanging information and communication has
become convenient. However, on the other hand, the
leakage of personal information and organizational
assets has become a significant problem. As a coun-
termeasure, there is a technique called steganography.
Steganography is the art of concealing one piece of
digital data (audio, images) within another piece of
digital data.
In a previous research(Kumabuchi and Kobayashi,
2022), two models were created using deep learning:
one to embed QR codes into images and the other to
restore QR codes from images with embedded QR
codes. However, there was a significant issue with
embedding QR codes into images, as it substantially
compromised the quality of the original images. In
this research, similar to the previous study, we aim
to create new Encoder and Decoder models to embed
QR codes into images and restore them without com-
promising the quality of the original images. We pro-
pose and evaluate a model capable of achieving this
goal
In a previous research, we referred to the model
proposed by Simon J
´
egou(J
´
egou et al., 2017). for
semantic segmentation, which improved upon the
Unet(Ronneberger et al., 2015) model, and used it as
a
https://orcid.org/0009-0004-0181-4185
b
https://orcid.org/0000-0002-4110-3570
a basis for our work. In this study, we further re-
fined that model to devise a method for embedding
QR codes while preserving their distinctive features.
2 PRINCIPLE
In this PRINCIPLE, the embedding and restoration
procedures of the QR code are explained with the aid
of Figure1, along with the learning steps.
1. Input a three-channel image and a one-channel
QR code into two separate models.
2. Concatenate the two output feature maps at an in-
termediate layer and input them into the Conca-
tImageModel.
3. In the QR code embedding model, train with the
three-channel image as the ground truth.
4. Pseudo-image and normalize it before inputting it
into the Restoration Model.
5. Train the Restoration Model using the output im-
age as input and the one-channel QR code image
as the ground truth.
6. Next, compute the loss for both models using the
Mean Squared Error (MSE) from the following
equation (1), and then calculate the weighted loss
using the following equation (2).
7. Use the computed loss values to update the
weights of both models.
342
Kumabuchi, K. and Kobayashi, H.
Proposal of a New Approach Using Deep Learning for QR Code Embedding.
DOI: 10.5220/0012238900003543
In Proceedings of the 20th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2023) - Volume 1, pages 342-345
ISBN: 978-989-758-670-5; ISSN: 2184-2809
Copyright © 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)